Okayasu Hiromasa, Brown Alexandra E, Nzioki Michael M, Gasasira Alex N, Takane Marina, Mkanda Pascal, Wassilak Steven G F, Sutter Roland W
Research, Policy and Product Development, Global Polio Eradication Department, World Health Organization, Geneva, Switzerland.
Country Office, World Health Organization, Abuja, Nigeria.
J Infect Dis. 2014 Nov 1;210 Suppl 1(Suppl 1):S341-6. doi: 10.1093/infdis/jiu162.
To assess the quality of supplementary immunization activities (SIAs), the Global Polio Eradication Initiative (GPEI) has used cluster lot quality assurance sampling (C-LQAS) methods since 2009. However, since the inception of C-LQAS, questions have been raised about the optimal balance between operational feasibility and precision of classification of lots to identify areas with low SIA quality that require corrective programmatic action.
To determine if an increased precision in classification would result in differential programmatic decision making, we conducted a pilot evaluation in 4 local government areas (LGAs) in Nigeria with an expanded LQAS sample size of 16 clusters (instead of the standard 6 clusters) of 10 subjects each.
The results showed greater heterogeneity between clusters than the assumed standard deviation of 10%, ranging from 12% to 23%. Comparing the distribution of 4-outcome classifications obtained from all possible combinations of 6-cluster subsamples to the observed classification of the 16-cluster sample, we obtained an exact match in classification in 56% to 85% of instances.
We concluded that the 6-cluster C-LQAS provides acceptable classification precision for programmatic action. Considering the greater resources required to implement an expanded C-LQAS, the improvement in precision was deemed insufficient to warrant the effort.
为评估补充免疫活动(SIAs)的质量,全球根除脊髓灰质炎行动(GPEI)自2009年以来一直使用整群批质量保证抽样(C-LQAS)方法。然而,自C-LQAS开始实施以来,对于在操作可行性与确定需要采取纠正性项目行动的低SIA质量地区的批次分类精度之间的最佳平衡,一直存在疑问。
为确定分类精度的提高是否会导致不同的项目决策,我们在尼日利亚的4个地方政府辖区(LGAs)进行了一项试点评估,将LQAS样本量扩大到16个整群(而非标准的6个整群),每个整群10名受试者。
结果显示,整群之间的异质性大于假定的10%标准差,范围在12%至23%之间。将从6个整群子样本的所有可能组合中获得的4种结果分类分布与16个整群样本的观察分类进行比较,我们在56%至85%的情况下获得了完全匹配的分类。
我们得出结论,6个整群的C-LQAS为项目行动提供了可接受的分类精度。考虑到实施扩大的C-LQAS需要更多资源,精度的提高被认为不足以保证付出这样的努力。